Behavioral finance

Stock market is affected by news and information. If the stock market is not efficient, the reaction of stock price to news and information will place the stock market in overreaction and under-reaction states. Many models have been already presented by using different tools and techniques to forecast the stock market behavior. In this study, the reaction of stock price in the stock market was modeled by the behavioral finance approach. The population of this study included the companies listed on the Tehran Stock Exchange. In order to forecast the stock price, the final price data of the end December, March, June, and September 2006-2015 and the stock prices of 2014 and 2015 were analyzed as the sample. In this study, Bayes' rule was used to estimate the probability of the model change. Through this rule, the probability of an event can be calculated by conditioning the occurrence or lack of occurrence of another event. The results of model estimation showed that there is the probability of being placed in high-fluctuated regimes (overreaction) and low- fluctuated (under-reaction of stock price despite the shocks entered to the stock market. In modelling with the 4-month final prices, it was proved that the real stock price had no difference from the market price.

Investigating the performance of companies is one of the most important issues for the users of accounting information. The purpose of this study was to provide evidence about the effect of asymmetric appropriateness on investors' expectations. The results of the research on accepted companies In the Tehran Stock Exchange during the period from 2012 to 2016. To collect data, a library method was used and referring to financial statements, explanatory notes and monthly stock exchange of the Stock Exchange. Using Cochran sampling method, 120 firms were selected as the statistical sample. To analyze the data, multivariate regression analysis was used to confirm the rejection and research hypotheses (ivyz software). The results showed that asymmetric accountability leads to different expectations of investors about the company's outlook, and the negative relationship between asymmetric verification (conservatism) and investors' controversy is weaker with the release of good news and gets worse by the publication of bad news. The results are consistent with the documentation referred to in the theoretical framework of financial research and literature, such as the theory of vision and the concepts of behavioral and financial behavior, and the publication of good and bad news has a significant role.

Recently, understanding the anomalies in financial markets have severely chal-lenged the efficient market hypothesis (EMH). The price momentum is one of the anomalies described as the unexplained short-term return by Fama and French (1996). The present research strives for modeling the price momentum of winner stock in the Iranian capital market. The grounded theory method was used to explain this phenomenon. To this end, in-depth interviews were held with 32 experts operating in the professional and academic fields in 2018. The collected data was encoded in three steps, and the results were presented as a conceptual paradigm. The research findings identified the momentum causal factors in the behavioral level, the background factors in the social, macroeconomics, and mar-ket levels, the intervening factors in the global economics, macroeconomics, mar-ket, and company levels, and the strategies in the social, macroeconomics, market, the investment and finances institutions, and consequences factors in market level. The research findings suggest that the winner stock price momentum phenomenon should not be considered a speculation opportunity. Rather, it is an anomaly that has to be regulated with the proposed strategies according to the experts. The consequences of the adoption of these strategies include the stable and normal income for the market actors, the decrease in the loss inflicted on natural persons due to the market volatility, the management of anomalies, more effective attrac-tion and allocation of liquid capitals, the reduced credit risk of brokerages, and the acceleration of liquidation in the market.

Classic finance believes that stock price changes are related to systematic changes in the company's intrinsic values. However, recent research shows that behavioral factors play a very important role in determining stock prices and returns of investors, one of these behavioral patterns is noise trading. The purpose of this study is to investigate the effect of unsystematic risk fluctuations on noise transactions.    For this study, we use the random variance of the capital asset pricing model-disrupted unit as a measure of unsystematic risk fluctuations and for measuring noise trading We used a comparison of  company market value with industry companies the average market value. The research sample included 92 companies listed in the Tehran Stock Exchange during the period of 2011-2016.       The result of the test the hypothesis of the research showed that the relationship between unsystematic risk fluctuations and noise trading using is positive and significant and thus unsystematic risk fluctuations can be used as a criterion for detecting noise trading.

This paper investigates the design of an efficient model so as to anticipate the basic economic market rate of returns. To do so, accepting the relationships, interactions and effectiveness of these markets and exploiting Comonotonic Functions under Probability Function Framework as well as using weekly data for ten years’ period of time(2008-2017) in Iran’s economy we design optimum model and test its capability and estimation power. The results illustrate the efficiency of the achieved model. Furthermore, taking the practical nature of this paper into account, we come up with optimum lag of time and the period of time required to achieve equilibrium in any market and the entire economy as a prototype in the frame of Stock Exchange.

Behavioral finance had been becoming a fast-growing field of study in the past few years and because of the importance of investors' behavior in market performance, it's extremely noteworthy. By studying biases from their orientation perspective, we can divide them into two major groups, past-oriented, and current-oriented biases. In this research, a model had been developed for the past-oriented behavioral bias, which is closely related to the random walk theory. The research sample included the daily price information of 9 different industry indices in the Tehran Exchange Price Index (TEPIX), the index of 50 Top Companies in the Tehran Stock Exchange, and the S&P index in the New York Stock Exchange from 03/25/2011 to 03/19/2019. The results of the ARIMA model based on Markov switching models were measured for the degree of rigidity of these indexes by random walk theory, and then the effect of past-oriented behavioral bias was calculated in each of these 12 indexes by developing a new model. The results indicate that the cement index had the highest past-oriented behavioral bias (57%), followed by the top 50 companies index (46%), chemicals (41%), and oil product index (12%). However, the S&P index had no past-oriented behavioral bias.

One of the characteristics of the financial market, especially the stock market, is the effects of behavioral factors and on other financial and non-financial markets. There are several factors that affect the return of a stock exchange. We can refer to political, socio-cultural, technological and finally economic factors. A stock market is an economic market in which securities are traded under specific rules and regulations. Accordingly, in this study, the effect of behavioral financial arguments and other financial markets on stock market returns based on quantitative analysis has been studied. This article tries to examine how exchange rates, gold, and oil as key factors of a model can explain fluctuations of the stock market index. so the effect of those variables on the stock market index in the period 2008 to the first six months of 2018 has been analyzed using the FIAPGARCH-X model. The results of the analysis show that the effect of exchange rates on the stock market fluctuations is greater than the other two factors. The results also indicate that there are asymmetric effects of increased returns on the stock market, which is consistent with behavioral bias in behavioral finance.

The purpose of this study was to investigate the effect of peripheral uncertainty on the relationship between the herd behavior of investors and the price of negative stock shock. Because the capital market is one of the main pillars of the country's economic growth and development, the incidence of any disturbance and deviation in prices causes problems with allocating and equipping funds. Reducing stock prices is an example of the disorders created in the capital market. On the other hand, one of the factors that lead to the fluctuations of return and instability of financial markets is the herd behavior of investors. If investors lack sufficient information about environmental factors, environmental uncertainty occurs. Such a situation also affects the organization. Environmental uncertainty overshadowed the use of financial statements. Changes in stock prices and corporate status are cases that can not speak in full confidence in their occurrence. As a result, environmental uncertainty is part of the economic environment. To test the research hypotheses, 156 companies from the Tehran Stock Exchange during the years 2010 to 2020 were selected by screening method. To test the hypotheses, multivariate regression models were performed using STATA14 software. The results indicate that herd behavior has a significant effect on price shock and environmental uncertainty has no effect on the relationship between the two.

The occurrence of unexpected phenomena in recent decades in financial markets around the world,led to the development of theories,beyond the defined principles and criteria of classic finance.These theories are based on financial psychology and they are explaining the role of psychology in financial sciences as an influential factor and became irreplaceable in financial markets and investor decisions.One of the important theories in this field is the bounded-rationality-theory,which can explain the behavior of decision-makers about financial and economical issues based on defined theoretical frameworks and assumptions.This study developed the stock pricing model by comparing the predicted stock price based on the bounded-rationality-theory and the real stock price through collecting the data of 122 companies listed on the Tehran Stock Exchange during the period 2011to 2019.The results of this study show the effectiveness of the bounded-rationality-theory based on the separation of stock return components and measuring the irrationality coefficient and emotional reactions of investors' decisions in stock prices.Accordingly,the limitations of investors 'ability to process information seem to affect the level of use of reasoning and rationality in decision-making and the effect of bounded-rationality through the irrationality and limited attention on stock pricing.Therefore,it is expected that knowledge about the process of bounded-rationality based on the rational-bounded of investors and the behavioral biases resulting from the irrational part of their thinking,will provide a good explanation for the process of changes in financial markets.This can provide both profit opportunities and costs in investment management so that it can be used in modeling,analysis and investment strategies.

Investor expectations future economic processes are among the crucial factors affecting their decisions. The expectations seem to play a specific role since they are unsupervised variables capable of forming observable economic phenomena. Psychological factors influence investor expectations and corporate market value. Investor sentiments was modelled with an emphasis on psychological factors based on the Grounded Theory (GT). This applied and mixed-methods at its first and second stages. The statistical population comprised 13 experts, senior managers of investment companies, and university professors. The participants were selected through purposive and snowball sampling and the process was continued until theoretical saturation. The data were collected via semi-structured interviews coded via Atlas.ta.8 software. The research data were analysed using an open coding method. The results of the research were presented in 46 categories and 6 key dimensions.